32 citations · 75 across the 17 of their papers we have counts for
12 papers · 1 filter
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition
Chao-Han Huck Yang, Taejin Park, Yuan Gong +18
Given recent advances in generative AI technology, a key question is how large language models (LLMs) can enhance acoustic modeling tasks using text decoding results from a frozen,…
DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data
Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +5
Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs) by incorporating pre-trained speech models. However, these SLMs…
Chain-of-Thought Prompting for Speech Translation
Ke Hu, Zhehuai Chen, Chao-Han Huck Yang +5
Large language models (LLMs) have demonstrated remarkable advancements in language understanding and generation. Building on the success of text-based LLMs, recent research has ada…
Benchmarking Japanese Speech Recognition on ASR-LLM Setups with Multi-Pass Augmented Generative Error Correction
Yuka Ko, Sheng Li, Chao-Han Huck Yang +1
With the strong representational power of large language models (LLMs), generative error correction (GER) for automatic speech recognition (ASR) aims to provide semantic and phonet…
Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation Models
Yuchen Hu, Chen Chen, Chao-Han Huck Yang +4
We propose an unsupervised adaptation framework, Self-TAught Recognizer (STAR), which leverages unlabeled data to enhance the robustness of automatic speech recognition (ASR) syste…
An Investigation of Incorporating Mamba for Speech Enhancement
Rong Chao, Wen-Huang Cheng, Moreno La Quatra +4
This work aims to investigate the use of a recently proposed, attention-free, scalable state-space model (SSM), Mamba, for the speech enhancement (SE) task. In particular, we emplo…
Bayesian Example Selection Improves In-Context Learning for Speech, Text, and Visual Modalities
Siyin Wang, Chao-Han Huck Yang, Ji Wu +1
Large language models (LLMs) can adapt to new tasks through in-context learning (ICL) based on a few examples presented in dialogue history without any model parameter update. Desp…
It's Never Too Late: Fusing Acoustic Information into Large Language Models for Automatic Speech Recognition
Chen Chen, Ruizhe Li, Yuchen Hu +4
Recent studies have successfully shown that large language models (LLMs) can be successfully used for generative error correction (GER) on top of the automatic speech recognition (…
GenTranslate: Large Language Models are Generative Multilingual Speech and Machine Translators
Yuchen Hu, Chen Chen, Chao-Han Huck Yang +4
Recent advances in large language models (LLMs) have stepped forward the development of multilingual speech and machine translation by its reduced representation errors and incorpo…
Investigating Training Strategies and Model Robustness of Low-Rank Adaptation for Language Modeling in Speech Recognition
Yu Yu, Chao-Han Huck Yang, Tuan Dinh +10
The use of low-rank adaptation (LoRA) with frozen pretrained language models (PLMs) has become increasing popular as a mainstream, resource-efficient modeling approach for memory-c…
Large Language Models are Efficient Learners of Noise-Robust Speech Recognition
Yuchen Hu, Chen Chen, Chao-Han Huck Yang +4
Recent advances in large language models (LLMs) have promoted generative error correction (GER) for automatic speech recognition (ASR), which leverages the rich linguistic knowledg…
Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks
Kevin Everson, Yile Gu, Huck Yang +10
In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with tr…